{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('pruebas.csv')\n",
    "df = df[df[\"Language Model\"]==\"google/canine-c\"]\n",
    "df = df[df[\"Finetuned\"]==\"not_finetuned\"]\n",
    "df = df[df[\"Encoding\"]==\"rel-pos\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treebank</th>\n",
       "      <th>Encoding</th>\n",
       "      <th>Language Model</th>\n",
       "      <th>Finetuned</th>\n",
       "      <th>Pretrained</th>\n",
       "      <th>UAS</th>\n",
       "      <th>LAS</th>\n",
       "      <th>CLAS</th>\n",
       "      <th>MLAS</th>\n",
       "      <th>BLEX</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UD_Ancient_Greek-Perseus</td>\n",
       "      <td>rel-pos</td>\n",
       "      <td>google/canine-c</td>\n",
       "      <td>not_finetuned</td>\n",
       "      <td>pretrained</td>\n",
       "      <td>24.42</td>\n",
       "      <td>16.19</td>\n",
       "      <td>7.57</td>\n",
       "      <td>3.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>UD_Ancient_Greek-Perseus</td>\n",
       "      <td>rel-pos</td>\n",
       "      <td>google/canine-c</td>\n",
       "      <td>not_finetuned</td>\n",
       "      <td>not_pretrained</td>\n",
       "      <td>21.13</td>\n",
       "      <td>10.07</td>\n",
       "      <td>7.57</td>\n",
       "      <td>3.82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>UD_Skolt_Sami-Giellagas</td>\n",
       "      <td>rel-pos</td>\n",
       "      <td>google/canine-c</td>\n",
       "      <td>not_finetuned</td>\n",
       "      <td>pretrained</td>\n",
       "      <td>30.77</td>\n",
       "      <td>18.76</td>\n",
       "      <td>12.06</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>UD_Skolt_Sami-Giellagas</td>\n",
       "      <td>rel-pos</td>\n",
       "      <td>google/canine-c</td>\n",
       "      <td>not_finetuned</td>\n",
       "      <td>not_pretrained</td>\n",
       "      <td>5.26</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>UD_Welsh-CCG</td>\n",
       "      <td>rel-pos</td>\n",
       "      <td>google/canine-c</td>\n",
       "      <td>not_finetuned</td>\n",
       "      <td>pretrained</td>\n",
       "      <td>57.89</td>\n",
       "      <td>42.46</td>\n",
       "      <td>33.46</td>\n",
       "      <td>3.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>UD_Welsh-CCG</td>\n",
       "      <td>rel-pos</td>\n",
       "      <td>google/canine-c</td>\n",
       "      <td>not_finetuned</td>\n",
       "      <td>not_pretrained</td>\n",
       "      <td>33.02</td>\n",
       "      <td>10.84</td>\n",
       "      <td>7.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>UD_Bulgarian-BTB</td>\n",
       "      <td>rel-pos</td>\n",
       "      <td>google/canine-c</td>\n",
       "      <td>not_finetuned</td>\n",
       "      <td>pretrained</td>\n",
       "      <td>60.30</td>\n",
       "      <td>53.28</td>\n",
       "      <td>43.73</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>UD_Bulgarian-BTB</td>\n",
       "      <td>rel-pos</td>\n",
       "      <td>google/canine-c</td>\n",
       "      <td>not_finetuned</td>\n",
       "      <td>not_pretrained</td>\n",
       "      <td>28.60</td>\n",
       "      <td>14.68</td>\n",
       "      <td>3.34</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>UD_Guajajara-TuDeT</td>\n",
       "      <td>rel-pos</td>\n",
       "      <td>google/canine-c</td>\n",
       "      <td>not_finetuned</td>\n",
       "      <td>pretrained</td>\n",
       "      <td>55.63</td>\n",
       "      <td>33.45</td>\n",
       "      <td>24.34</td>\n",
       "      <td>6.27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>UD_Guajajara-TuDeT</td>\n",
       "      <td>rel-pos</td>\n",
       "      <td>google/canine-c</td>\n",
       "      <td>not_finetuned</td>\n",
       "      <td>not_pretrained</td>\n",
       "      <td>42.69</td>\n",
       "      <td>8.74</td>\n",
       "      <td>10.27</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>UD_Armenian-ArmTDP</td>\n",
       "      <td>rel-pos</td>\n",
       "      <td>google/canine-c</td>\n",
       "      <td>not_finetuned</td>\n",
       "      <td>pretrained</td>\n",
       "      <td>47.17</td>\n",
       "      <td>34.17</td>\n",
       "      <td>23.37</td>\n",
       "      <td>3.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>UD_Armenian-ArmTDP</td>\n",
       "      <td>rel-pos</td>\n",
       "      <td>google/canine-c</td>\n",
       "      <td>not_finetuned</td>\n",
       "      <td>not_pretrained</td>\n",
       "      <td>21.62</td>\n",
       "      <td>7.63</td>\n",
       "      <td>8.02</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>UD_Turkish-BOUN</td>\n",
       "      <td>rel-pos</td>\n",
       "      <td>google/canine-c</td>\n",
       "      <td>not_finetuned</td>\n",
       "      <td>pretrained</td>\n",
       "      <td>39.59</td>\n",
       "      <td>26.31</td>\n",
       "      <td>18.89</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>UD_Turkish-BOUN</td>\n",
       "      <td>rel-pos</td>\n",
       "      <td>google/canine-c</td>\n",
       "      <td>not_finetuned</td>\n",
       "      <td>not_pretrained</td>\n",
       "      <td>6.47</td>\n",
       "      <td>1.63</td>\n",
       "      <td>1.89</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>UD_Ligurian-GLT</td>\n",
       "      <td>rel-pos</td>\n",
       "      <td>google/canine-c</td>\n",
       "      <td>not_finetuned</td>\n",
       "      <td>pretrained</td>\n",
       "      <td>34.17</td>\n",
       "      <td>21.83</td>\n",
       "      <td>7.03</td>\n",
       "      <td>2.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>UD_Ligurian-GLT</td>\n",
       "      <td>rel-pos</td>\n",
       "      <td>google/canine-c</td>\n",
       "      <td>not_finetuned</td>\n",
       "      <td>not_pretrained</td>\n",
       "      <td>23.75</td>\n",
       "      <td>10.89</td>\n",
       "      <td>1.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>UD_Vietnamese-VTB</td>\n",
       "      <td>rel-pos</td>\n",
       "      <td>google/canine-c</td>\n",
       "      <td>not_finetuned</td>\n",
       "      <td>pretrained</td>\n",
       "      <td>37.70</td>\n",
       "      <td>26.14</td>\n",
       "      <td>22.98</td>\n",
       "      <td>15.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>UD_Vietnamese-VTB</td>\n",
       "      <td>rel-pos</td>\n",
       "      <td>google/canine-c</td>\n",
       "      <td>not_finetuned</td>\n",
       "      <td>not_pretrained</td>\n",
       "      <td>21.10</td>\n",
       "      <td>7.43</td>\n",
       "      <td>8.73</td>\n",
       "      <td>7.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>UD_Basque-BDT</td>\n",
       "      <td>rel-pos</td>\n",
       "      <td>google/canine-c</td>\n",
       "      <td>not_finetuned</td>\n",
       "      <td>pretrained</td>\n",
       "      <td>18.38</td>\n",
       "      <td>12.25</td>\n",
       "      <td>9.65</td>\n",
       "      <td>2.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>UD_Basque-BDT</td>\n",
       "      <td>rel-pos</td>\n",
       "      <td>google/canine-c</td>\n",
       "      <td>not_finetuned</td>\n",
       "      <td>not_pretrained</td>\n",
       "      <td>8.55</td>\n",
       "      <td>3.36</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>UD_Bhojpuri-BHTB</td>\n",
       "      <td>rel-pos</td>\n",
       "      <td>google/canine-c</td>\n",
       "      <td>not_finetuned</td>\n",
       "      <td>pretrained</td>\n",
       "      <td>35.14</td>\n",
       "      <td>22.47</td>\n",
       "      <td>13.77</td>\n",
       "      <td>1.51</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>UD_Bhojpuri-BHTB</td>\n",
       "      <td>rel-pos</td>\n",
       "      <td>google/canine-c</td>\n",
       "      <td>not_finetuned</td>\n",
       "      <td>not_pretrained</td>\n",
       "      <td>23.22</td>\n",
       "      <td>7.99</td>\n",
       "      <td>9.63</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>UD_Kiche-IU</td>\n",
       "      <td>rel-pos</td>\n",
       "      <td>google/canine-c</td>\n",
       "      <td>not_finetuned</td>\n",
       "      <td>pretrained</td>\n",
       "      <td>71.99</td>\n",
       "      <td>61.13</td>\n",
       "      <td>52.52</td>\n",
       "      <td>19.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>UD_Kiche-IU</td>\n",
       "      <td>rel-pos</td>\n",
       "      <td>google/canine-c</td>\n",
       "      <td>not_finetuned</td>\n",
       "      <td>not_pretrained</td>\n",
       "      <td>25.88</td>\n",
       "      <td>7.26</td>\n",
       "      <td>9.08</td>\n",
       "      <td>2.41</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Treebank Encoding   Language Model      Finetuned  \\\n",
       "4   UD_Ancient_Greek-Perseus  rel-pos  google/canine-c  not_finetuned   \n",
       "5   UD_Ancient_Greek-Perseus  rel-pos  google/canine-c  not_finetuned   \n",
       "10   UD_Skolt_Sami-Giellagas  rel-pos  google/canine-c  not_finetuned   \n",
       "11   UD_Skolt_Sami-Giellagas  rel-pos  google/canine-c  not_finetuned   \n",
       "16              UD_Welsh-CCG  rel-pos  google/canine-c  not_finetuned   \n",
       "17              UD_Welsh-CCG  rel-pos  google/canine-c  not_finetuned   \n",
       "22          UD_Bulgarian-BTB  rel-pos  google/canine-c  not_finetuned   \n",
       "23          UD_Bulgarian-BTB  rel-pos  google/canine-c  not_finetuned   \n",
       "28        UD_Guajajara-TuDeT  rel-pos  google/canine-c  not_finetuned   \n",
       "29        UD_Guajajara-TuDeT  rel-pos  google/canine-c  not_finetuned   \n",
       "34        UD_Armenian-ArmTDP  rel-pos  google/canine-c  not_finetuned   \n",
       "35        UD_Armenian-ArmTDP  rel-pos  google/canine-c  not_finetuned   \n",
       "40           UD_Turkish-BOUN  rel-pos  google/canine-c  not_finetuned   \n",
       "41           UD_Turkish-BOUN  rel-pos  google/canine-c  not_finetuned   \n",
       "46           UD_Ligurian-GLT  rel-pos  google/canine-c  not_finetuned   \n",
       "47           UD_Ligurian-GLT  rel-pos  google/canine-c  not_finetuned   \n",
       "52         UD_Vietnamese-VTB  rel-pos  google/canine-c  not_finetuned   \n",
       "53         UD_Vietnamese-VTB  rel-pos  google/canine-c  not_finetuned   \n",
       "58             UD_Basque-BDT  rel-pos  google/canine-c  not_finetuned   \n",
       "59             UD_Basque-BDT  rel-pos  google/canine-c  not_finetuned   \n",
       "64          UD_Bhojpuri-BHTB  rel-pos  google/canine-c  not_finetuned   \n",
       "65          UD_Bhojpuri-BHTB  rel-pos  google/canine-c  not_finetuned   \n",
       "70               UD_Kiche-IU  rel-pos  google/canine-c  not_finetuned   \n",
       "71               UD_Kiche-IU  rel-pos  google/canine-c  not_finetuned   \n",
       "\n",
       "        Pretrained    UAS    LAS   CLAS   MLAS  BLEX  Unnamed: 10  \n",
       "4       pretrained  24.42  16.19   7.57   3.35   0.0          NaN  \n",
       "5   not_pretrained  21.13  10.07   7.57   3.82   0.0          NaN  \n",
       "10      pretrained  30.77  18.76  12.06   0.99   0.0          NaN  \n",
       "11  not_pretrained   5.26   1.35   1.70   0.00   0.0          NaN  \n",
       "16      pretrained  57.89  42.46  33.46   3.10   0.0          NaN  \n",
       "17  not_pretrained  33.02  10.84   7.12   0.00   0.0          NaN  \n",
       "22      pretrained  60.30  53.28  43.73   0.99   0.0          NaN  \n",
       "23  not_pretrained  28.60  14.68   3.34   0.02   0.0          NaN  \n",
       "28      pretrained  55.63  33.45  24.34   6.27   0.0          NaN  \n",
       "29  not_pretrained  42.69   8.74  10.27   1.38   0.0          NaN  \n",
       "34      pretrained  47.17  34.17  23.37   3.17   0.0          NaN  \n",
       "35  not_pretrained  21.62   7.63   8.02   0.28   0.0          NaN  \n",
       "40      pretrained  39.59  26.31  18.89   2.00   0.0          NaN  \n",
       "41  not_pretrained   6.47   1.63   1.89   0.20   0.0          NaN  \n",
       "46      pretrained  34.17  21.83   7.03   2.13   0.0          NaN  \n",
       "47  not_pretrained  23.75  10.89   1.64   0.00   0.0          NaN  \n",
       "52      pretrained  37.70  26.14  22.98  15.87   0.0          NaN  \n",
       "53  not_pretrained  21.10   7.43   8.73   7.78   0.0          NaN  \n",
       "58      pretrained  18.38  12.25   9.65   2.39   0.0          NaN  \n",
       "59  not_pretrained   8.55   3.36   1.70   0.37   0.0          NaN  \n",
       "64      pretrained  35.14  22.47  13.77   1.51   0.0          NaN  \n",
       "65  not_pretrained  23.22   7.99   9.63   1.22   0.0          NaN  \n",
       "70      pretrained  71.99  61.13  52.52  19.96   0.0          NaN  \n",
       "71  not_pretrained  25.88   7.26   9.08   2.41   0.0          NaN  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Treebank', ylabel='LAS'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Treebank', y='LAS', hue='Pretrained', data=df, palette=\"Set2\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ml_probing",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "085035040a5f8541f0a11a144df6817fe51afa0926562ddfedc7359bee17119a"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
